Solid state image sensors are well known in the art. They are used, for example, in video cameras and fax machines. In a conventional image sensor, image processing occurs in two stages: preprocessing and postprocessing.
In accordance with the real time image preprocessing stage, image data is retrieved from photoelectric conversion elements (e.g. photodiodes) and stored in a frame memory. Once the image data is stored in the frame memory, it is processed to provide edge detection or image smoothing. Image smoothing is a process by which noise is removed from the photoelectric conversion element data. Edge detection is a process by which the edge of an input image is detected. In image input devices with solid state image pick-up elements, the image data retrieval is generally performed using a local mask. Local masks perform peripheral processing in an area, for example, from 2 by 2 photoelectric conversion elements to 9 by 9 photoelectric conversion elements. This peripheral processing is performed over the entire image and it is necessary to access each photoelectric conversion element's data repeatedly to complete the image pre-processing.
Photoelectric conversion elements which enable non-destructive readout of light signals have also been used for image preprocessing such as edge detection and image smoothing. Non-destructive readout (NDRO) photoelectric conversion elements, such as charge modulation devices (CMD) and static induction transistors (SIT), enable faster pre-processing of an image while utilizing a simpler circuit because it eliminates the need for a frame memory. More specifically, since the charge of an NDRO photoelectric conversion element can be read repeatedly, there is no need to store the image data in a separate storage device. Unfortunately, prior art image processors utilizing NDRO photoelectric conversion elements still require a processor including, for example, a multiplier and adder, because in order to provide a smoothed output for a given photoelectric conversion element, a weighted average of the data from several photoelectric conversion elements must be obtained. Such a process can become computationally intensive thereby increasing the size and cost of the system. For example, to produce a weighted average for a 2.times.2 local mask requires 4 analog multipliers and an adder. For a 3.times.3 local mask, 9 multipliers are required.
In order to provide edge detection, prior art circuits compared the photoelectric conversion element data for each photoelectric conversion element with an average of the photoelectric conversion element data from some or all of the other photoelectric conversion elements (e.g., the smoothed output). If the difference changes drastically over a short distance, an edge is detected. Thus, edge detection requires even more processing than image smoothing.
Another prior art image preprocessing technique, called "thresholding", is used to produce a two-valued (binary) image. For example, in a facsimile machine, it is often desirable to vary the threshold at which the machine recognizes the presence (or absence) of a point on the page. Such a variable threshold is necessary, for example, where the document has a homogeneous background (e.g. blue paper as opposed to white paper). In prior art systems, a variable threshold was calculated based upon previously stored photoelectric conversion element data. Such systems required an additional processor to analyze this previously stored photoelectric conversion element data and to provide a new threshold to compensate for the background level on the document.
For example, it is known in the prior art to convert the charge at each photoelectric conversion element into a digital value using an A/D converter and to store the digitized data from all the photoelectric conversion elements in a frame memory. It is known that a proper threshold (the point at which the image sensor distinguishes between "text" and "background") can be set by providing a graph (histogram) which shows the number of photoelectric conversion elements as a function of charge level. Although several algorithms have been developed to determine the threshold value based on such a histogram, these methods require massive computations.
Moreover, in some cases, different portions of a document have different background levels. In order to compensate for such stains or shading in a document, prior art systems derived several local thresholds in the manner described above from a plurality of histograms thereby further increasing the complexity and cost of processing.
Therefore, a need exists for a solid state image sensor which provides signal processing while eliminating the above-mentioned problems of the prior art.